8/3/2019 Conformed Dimension

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Conformed dimension

A conformed dimension is a set of data attributes that have been physically implemented in multiple database tables using the same

structure, attributes, domain values, definitions and concepts in each implementation. A conformed dimension cuts across manyfacts.

Dimensions are conformed when they are either exactly the same (including keys) or one is a perfect subset of the other. Most

important, the row headers produced in the answer sets from two different conformed dimensions must be able to match perfectly.

Conformed dimensions are either identical or strict mathematical subsets of the most granular, detailed dimension. Dimension

tables are not conformed if the attributes are labeled differently or contain different values. Conformed dimensions come in several

different flavors. At the most basic level, conformed dimensions mean exactly the same thing with every possible fact table to which

they are joined. The date dimension table connected to the sales facts is identical to the date dimension connected to the inventory

facts .[1]

[edit ]Junk dimension

A junk dimension is a convenient grouping of typically low-cardinality flags and indicators. By creating an abstract dimension, these

flags and indicators are removed from the fact table while placing them into a useful dimensional framework .[2] A Junk Dimension is

a dimension table consisting of attributes that do not belong in the fact table or in any of the existing dimension tables. The nature of

these attributes is usually text or various flags, e.g. non-generic comments or just simple yes/no or true/false indicators. These kinds

of attributes are typically remaining when all the obvious dimensions in the business process have been identified and thus thedesigner is faced with the challenge of where to put these attributes that do not belong in the other dimensions.

One solution is to create a new dimension for each of the remaining attributes, but due to their nature, it could be necessary tocreate a vast number of new dimensions resulting in a fact table with a very large number of foreign keys. The designer could also

decide to leave the remaining attributes in the fact table but this could make the row length of the table unnecessarily large if, for

example, the attributes is a long text string.

The solution to this challenge is to identify all the attributes and then put them into one or several Junk Dimensions. One Junk

Dimension can hold several true/false or yes/no indicators that have no correlation with each other, so it would be convenient to

convert the indicators into a more describing attribute. An example would be an indicator about whether a package had arrived,

instead of indicating this as yes or no, it would be converted into arrived or pending in the junk dimension. The designer can

choose to build the dimension table so it ends up holding all the indicators occurring with every other indicator so that all

combinations are covered. This sets up a fixed size for the table itself which would be 2^x rows, where x is the number of indicators.

This solution is appropriate in situations where the designer would expect to encounter a lot of di fferent combinations and where the

possible combinations are limited to an acceptable level. In a situation where the number of indicators are large, thus creating a very

big table or where the designer only expect to encounter a few of the possible combinations, it would be more appropriate to build

each row in the junk dimension as new combinations are encountered. To limit the size of the tables, multiple junk dimensions might

be appropriate in other situations depending on the correlation between various indicators.

Junk dimensions are also appropriate for placing attributes like non-generic comments from the fact table. Such attributes might

consist of data from an optional comment field when a customer places an order and as a result will probably be blank in many

cases. Therefore the junk dimension should contain a single row representing the blanks as a surrogate key that will be used in the

fact table for every row returned with a blank comment field [3]

A dimension key, such as a transaction number, invoice number, ticket number, or bill-of-lading number, that has no attributes and

hence does not join to an actual dimension table. Degenerate dimensions are very common when the grain of a fact tablerepresents a single transaction item or line item because the degenerate dimension represents the unique identifier of the parent.

Degenerate dimensions often play an integral role in the fact table's primary key .[4]

[edit ]Role-playing dimensions

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